Exploring polynomial classifier to predict match results in football championships. (15th October 2017)

• Record Type: Journal Article
• Authors: Martins, Rodrigo G.; Martins, Alessandro S.; Neves, Leandro A.; Lima, Luciano V.; Flores, Edna L.; do Nascimento, Marcelo Z.
• Is Part Of: Expert systems with applications. Volume 83(2017)
• Page Start: 79
• Page End: 93
• Publication Date: 2017-10-15
• Subjects: Polynomial classifier -- Football championship -- Prediction -- Feature selection -- Machine learning; Expert systems (Computer science) -- Periodicals; Systèmes experts (Informatique) -- Périodiques; Electronic journals; 006.33
• DOI: 10.1016/j.eswa.2017.04.040
• Languages: English
• ISSNs: 0957-4174

Abstract

Highlights: We present an approach to identify the winning team based on the polynomial classifier. The investigated groups were win-draw, win-defeat and draw-defeat. The features were evaluated by machine learning methods and the POL algorithm. The proposed approach achieved an accuracy superior to 96. It proposes the use of the POL algorithm as a method for feature selection. Abstract: Football is the team sport that mostly attracts great mass audience. Because of the detailed information about all football matches of championships over almost a century, matches build a huge and valuable database to test prediction of matches results. The problem of modeling football data has become increasingly popular in the last years and learning machine have been used to predict football matches results in many studies. Our present work brings a new approach to predict matches results of championships. This approach investigates data of matches in order to predict the results, which are win, draw and defeat. The investigated groups were different type of combinations of two by two pairs, win-draw, win-defeat and draw-defeat, of the possible matches results of each championship. In this study we employed the features obtained by scouts during a football match. The proposed system applies a polynomial algorithm to analyse and define matches results. Some machine-learning algorithms were compared with our approach, which includes experiments with information obtained from the football championships. The association between polynomial algorithm and machine learning techniques allowed a significant increase of the accuracy values. Our polynomial algorithm provided an accuracy superior to 96%, selecting the relevant features from the training and testing set.